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Machine Learning & Quantitative Finance
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Application of Artificial Intelligence Models for Assessing the Performance of Closed Vertical Refrigerated Display Cabinet - a Comparative Study of Different Operating Scenarios
3
2024/05/19
Application of Artificial Intelligence Models for Assessing the Performance of Closed Vertical Refrigerated Display Cabinet - a Comparative Study of Different Operating Scenarios
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danielmiessler/fabric
2024/05/19 16:57
10234
fabric is an open-source framework for augmenting humans using AI. It provides a modular framework for solving specific problems using a crowdsourced set of AI prompts that can be used anywhere.
danielmiessler/fabric
2024/05/19 16:57
10234
fabric is an open-source framework for augmenting humans using AI. It provides a modular framework for solving specific problems using a crowdsourced set of AI prompts that can be used anywhere.
metaskills/experts
2024/05/19 16:32
441
Experts.js is the easiest way to create and deploy OpenAI's Assistants and link them together as Tools to create advanced Multi AI Agent Systems with expanded memory and attention to detail.
metaskills/experts
2024/05/19 16:32
441
Experts.js is the easiest way to create and deploy OpenAI's Assistants and link them together as Tools to create advanced Multi AI Agent Systems with expanded memory and attention to detail.
nocobase/nocobase
2024/05/17 16:59
8315
NocoBase is a scalability-first, open-source no-code/low-code platform for building business applications and enterprise solutions.
nocobase/nocobase
2024/05/17 16:59
8315
NocoBase is a scalability-first, open-source no-code/low-code platform for building business applications and enterprise solutions.
51bitquant/binance_grid_trader
2024/05/17 09:25
726
Binance_grid_trader is a grid strategy bot trading with Binance Spot and Binance Futures Exchange. you can use it to trade any pair in Binance Exchange. Binance_grid_trader是一个币安网格策略软件, 目前支持币安现货，USDT合约和币币合约。
51bitquant/binance_grid_trader
2024/05/17 09:25
726
Binance_grid_trader is a grid strategy bot trading with Binance Spot and Binance Futures Exchange. you can use it to trade any pair in Binance Exchange. Binance_grid_trader是一个币安网格策略软件, 目前支持币安现货，USDT合约和币币合约。
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Twitter Trending
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@carlcarrie
Kolmogorov-Arnold Networks (KANs) for Time Series Analysis - paper posits that KANs are reasonable alternative to MLPs with several advantages…<a href="https://t.co/ofNs3aHRx9">https://t.co/ofNs3aHRx9</a> <a href="https://t.co/r5SO3t7QYA">pic.twitter.com/r5SO3t7QYA</a>
2024/05/18
@carlcarrie
Kolmogorov-Arnold Networks (KANs) for Time Series Analysis - paper posits that KANs are reasonable alternative to MLPs with several advantages…<a href="https://t.co/ofNs3aHRx9">https://t.co/ofNs3aHRx9</a> <a href="https://t.co/r5SO3t7QYA">pic.twitter.com/r5SO3t7QYA</a>
2024/05/18
@carlcarrie
Liquid Factor Models (yes, is an Alpha Model)<br><br>Paper:<a href="https://t.co/APV3eQV3ly">https://t.co/APV3eQV3ly</a> <a href="https://t.co/dMFiQU0MaO">pic.twitter.com/dMFiQU0MaO</a>
2024/05/16
@carlcarrie
Liquid Factor Models (yes, is an Alpha Model)<br><br>Paper:<a href="https://t.co/APV3eQV3ly">https://t.co/APV3eQV3ly</a> <a href="https://t.co/dMFiQU0MaO">pic.twitter.com/dMFiQU0MaO</a>
2024/05/16
@quantseeker
Technical Analysis and Currency Trading: False Discoveries and Informative Covariates<br>The paper below has recently been revised. " We apply the new method to a large universe of currency technical trading rules, and construct a dynamic 30-currency portfolio that generates a… <a href="https://t.co/Dr46C2xqvi">https://t.co/Dr46C2xqvi</a>
2024/05/15
@quantseeker
Technical Analysis and Currency Trading: False Discoveries and Informative Covariates<br>The paper below has recently been revised. " We apply the new method to a large universe of currency technical trading rules, and construct a dynamic 30-currency portfolio that generates a… <a href="https://t.co/Dr46C2xqvi">https://t.co/Dr46C2xqvi</a>
2024/05/15
@carlcarrie
Ode to New Diffusion Applications<a href="https://t.co/k1ax8z49iy">https://t.co/k1ax8z49iy</a> <a href="https://t.co/qhupnQU81Z">pic.twitter.com/qhupnQU81Z</a>
2024/05/15
@carlcarrie
Ode to New Diffusion Applications<a href="https://t.co/k1ax8z49iy">https://t.co/k1ax8z49iy</a> <a href="https://t.co/qhupnQU81Z">pic.twitter.com/qhupnQU81Z</a>
2024/05/15
@quantseeker
Seven Sins of Quantitative Investing<br>Nice discussion by Deutsche Bank on common pitfalls and mistakes when backtesting. Several illustrative examples are provided. See link in tweet below. <a href="https://t.co/yiGhVySvXC">https://t.co/yiGhVySvXC</a> <a href="https://t.co/qYJQpeiRkZ">pic.twitter.com/qYJQpeiRkZ</a>
2024/05/14
@quantseeker
Seven Sins of Quantitative Investing<br>Nice discussion by Deutsche Bank on common pitfalls and mistakes when backtesting. Several illustrative examples are provided. See link in tweet below. <a href="https://t.co/yiGhVySvXC">https://t.co/yiGhVySvXC</a> <a href="https://t.co/qYJQpeiRkZ">pic.twitter.com/qYJQpeiRkZ</a>
2024/05/14
@carlcarrie
Moment, a Time Series - <br>family of open-source foundation models for general-purpose time-series analysis (~LLM)<br><br>Python GitHub:<a href="https://t.co/Zj8Ppy0hAq">https://t.co/Zj8Ppy0hAq</a><br><br>Paper:<a href="https://t.co/FomYbtRhPc">https://t.co/FomYbtRhPc</a><br><br>Model Card:<a href="https://t.co/SESKZuxFeb">https://t.co/SESKZuxFeb</a> <a href="https://t.co/dZvHqJBN6X">pic.twitter.com/dZvHqJBN6X</a>
2024/05/13
@carlcarrie
Moment, a Time Series - <br>family of open-source foundation models for general-purpose time-series analysis (~LLM)<br><br>Python GitHub:<a href="https://t.co/Zj8Ppy0hAq">https://t.co/Zj8Ppy0hAq</a><br><br>Paper:<a href="https://t.co/FomYbtRhPc">https://t.co/FomYbtRhPc</a><br><br>Model Card:<a href="https://t.co/SESKZuxFeb">https://t.co/SESKZuxFeb</a> <a href="https://t.co/dZvHqJBN6X">pic.twitter.com/dZvHqJBN6X</a>
2024/05/13
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2024/05/19
Pandas code is now 50x faster on Google colab with zero code changes.
It now comes with native integration for RAPIDS cuDF, which powers GPU acceleration for pandas.
To boost your pandas code, use this single command at the top of your NVIDIA GPU-enabled Colab notebook:
%load_ext cudf.pandas
This feature can even accelerate the pandas code/queries generated by Colab AI, ChatGPT or any LLM-based chatbot, without needing to learn new paradigm.
Colab demo notebook: https://lnkd.in/gVSgsirJ
↓
Are you technical? Check out https://AlphaSignal.ai to get a weekly summary of the top trending models, repos and papers in AI. Read by 180,000+ engineers and researchers.
3
3611
2024/05/19
Now that the flow of testimonies about the passing of the legendary Jim Simons has faded, I think I can talk a bit about him. I noticed, by the way, that those I know who knew him the best remained rather silent, I guess out of respect for his stature.
Jim was one of the first people I met when I started studying financial math in 1994, as an old acquaintance of my father Adrien Douady.
It is difficult for me to speak of Jim without mentioning Dennis Sullivan, one of his closest collaborators and friends, himself a very close collaborator and friend of my father - almost an uncle to me (Dennis was awarded the Wolf Prize in 2010 and the Abel Prize in 2022, the equivalent for mathematics of a Nobel prize). This is to show the altitude in mathematical research at which Jim was flying. It is a bit delicate to make comparisons, but it needs to be said how different math research of this kind is to what we commonly call "financial engineering".
An anecdote about this: when I arrived in New York, and we decided, with the late Marco Avellaneda to start our math finance seminar at the Courant Institute (NYU) in 1995, we met Jim to ask for his support for the seminar (Renaissance Technology was already a very famous hedge fund). He was very kind and welcoming, then he asked: "By the way, what is mathematical finance?". He truly had no clue. For him, the math of financial markets was about statistics, microstructure, information theory. Pricing an option, the Black-Scholes model, wasn't his cup of tea.
We know Jim for #Renaissance and its flagship #Medallion fund. People speak of Chern-Simons classes, without really knowing what that means (some invariants in the algebraic classification of fiber bundles, but I'd need to explain what is a "fiber bundle", which I'm not going to do here, please check Wikipedia). Less known is the fact that he was part of Shannon's team on info
3
183
2024/05/19
I’d like to thank Professors Rama CONT, Blanka Horvath for inviting to speak at the Oxford Mathematical Institute seminar, and for moderating the talk.
I was excited to see a lot of interest in the audience for the use of information geometry and decision theory in portfolio construction. Thanks to all attendees for your questions and interesting conversations after the talk!
2
52
2024/05/19
Attention is All You Need" published by Google AI in 2017 is widely considered a foundational piece for building Large Language Models
Here's why it's so important:
Transformer Architecture: The paper introduced the Transformer, a novel neural network architecture. This architecture revolutionized how models process text data. Unlike previous models, the Transformer uses a self-attention mechanism, allowing it to understand the relationships between words in a sentence more effectively.
Unsupervised Learning: The Transformer also facilitated unsupervised learning for LLMs. This means LLMs could be trained on massive amounts of text data without the need for manually labeled examples, significantly improving their ability to learn language patterns.
While the Transformer isn't the only factor in LLM development, it's a major building block. Many of the most powerful LLMs today, including Gemini, utilize Transformer-based architectures as their core.
Checkout our course Machine Learning for Finance -https://lnkd.in/gtJDWcus
Separate modules for each AI and Machine Learning Type with exhaustive concepts.
15+ Real-World Practical Applications
Financial Applications Coverage
- Algo Trading
- Portfolio Management
- Fraud detection
- Lending and Loand Default prediction
- Sentiment Analysis
- Derivatives Pricing and Hedging
- Asset Price Prediction
- and many more
Course Description
Supervised Learning
Regression and Classification models
1. Linear and Logistic Regression
2. Random Forest and GBM
3. Deep Neural Network (including RNN and LSTM)
Includes 6+ case studies
Unsupervised Learning
Clustering and Dimensionality Reduction
1. Principal Component Analysis
2. k-Means and hierarchical clustering
Includes 5+ case studies
Reinforcement Learning and NLP
Value/Policy based RL models and sentiment analysis
1. Deep Q- Learning RL model
2. Policy-based RL models
2
40
2024/05/19
It seems to me that before "urgently figuring out how to control AI systems much smarter than us" we need to have the beginning of a hint of a design for a system smarter than a house cat.
Such a misplaced sense of urgency reveals an extremely distorted view of reality.
No wonder the more based members of the organization seeked to marginalize the superalignment group.
It's as if someone had said in 1925 "we urgently need to figure out how to control aircrafts that can transport hundreds of passengers at near the speed of sound over the oceans."
It would have been difficult to make long-haul passenger jets safe before the turbojet was invented and before any aircraft had crossed the atlantic non-stop.
Yet, we can now fly halfway around the world on twin-engine jets in complete safety.
It didn't require some sort of magical recipe for safety.
It took decades of careful engineering and iterative refinements.
The process will be similar for intelligent systems.
It will take years for them to get as smart as cats, and more years to get as smart as humans, let alone smarter (don't confuse the superhuman knowledge accumulation and retrieval abilities of current LLMs with actual intelligence).
It will take years for them to be deployed and fine-tuned for efficiency and safety as they are made smarter and smarter.
https://lnkd.in/eaJ5uuMk
1
4943
2024/05/19
I’ve worked in Data Science for a while. My journey into that field has been almost completely self taught. In my learning I have prioritized what is effective and works best, rather than some fancy high end tools or techniques that add unnecessary complexity. From everything I’ve seen over the years, here are my main takeaways:
* Python is good enough for 99.9% of tasks
* Jupyter is good enough for 99.9% of tasks
* Storing tabular data in CSV files is good enough for 99.9% of tasks
* Modeling your tabular data with XGBoost is good enough for 99.9% of tasks
* Working on your own laptop is good enough for 99.9% of tasks
* Working on CPU is good enough for 99.9% of tasks
* Installing libraries on bare metal is good enough for 99.9% of tasks
1
4940
2024/05/19
Ilya Sutskever of OpenAI gave John Carmack following reading list of approximately 30 research papers and said, ‘If you really learn all of these, you’ll know 90% of what matters today in AI.’ I have added few more LLM papers that potentially fills remaining ~9%
Here's Ilya's list: links here
https://lnkd.in/gVPEEejJ
1. The Annotated Transformer
2. The First Law of Complexodynamics
3. The Unreasonable Effectiveness of RNNs
4. Understanding LSTM Networks
5. Recurrent Neural Network Regularization
6. Keeping Neural Networks Simple by Minimizing the Description Length of the Weights
7. Pointer Networks
8. ImageNet Classification with Deep CNNs
9. Order Matters: Sequence to Sequence for Sets
10. GPipe: Efficient Training of Giant Neural Networks
11. Deep Residual Learning for Image Recognition
12. Multi-Scale Context Aggregation by Dilated Convolutions
13. Neural Quantum Chemistry
14. Attention Is All You Need
15. Neural Machine Translation by Jointly Learning to Align and Translate
16. Identity Mappings in Deep Residual Networks
17. A Simple NN Module for Relational Reasoning
18. Variational Lossy Autoencoder
19. Relational RNNs
20. Quantifying the Rise and Fall of Complexity in Closed Systems
21. Neural Turing Machines
22. Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
23. Scaling Laws for Neural LMs (arxiv.org)
24. A Tutorial Introduction to the Minimum Description Length Principle (arxiv.org)
25. Machine Super Intelligence Dissertation (vetta.org)
26. PAGE 434 onwards: Komogrov Complexity (lirmm.fr)
27. CS231n Convolutional Neural Networks for Visual Recognition (cs231n.github.io)
28. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 
29. BitNet: Scaling 1-bit Transformers for Large Language Models
30. KAN: Kolmogorov-Arnold Networks
Here are my recommended key LLM papers I would add on (the GPT, Llama, and Gemini paper
29. BitNet: Scaling 1-bit Transformers for Large Language Models
30. KAN: Kolmogorov-Arnold Networks
Here are my recommended key LLM papers I would add on (the GPT, Llama, and Gemini paper1
2182
2024/05/19
Ilya Sutskever gave John Carmack this reading list of approximately 30 research papers and said, ‘If you really learn all of these, you’ll know 90% of what matters today.’
Here's Ilya's list: https://lnkd.in/gHfsWd_u
Here are the key LLM papers I would add on (the GPT, Llama, and Gemini papers):
GPT-1: https://lnkd.in/gJ5Pe3HG
GPT-2: https://lnkd.in/gatQi8Ud
GPT-3: https://lnkd.in/g43GzYfZ
GPT-4: https://lnkd.in/ga_xEpEj
Llama-2: https://lnkd.in/gutaGW8h
Tools: https://lnkd.in/gqJ3aXpS
Gemini-Pro-1.5: https://lnkd.in/gbDcYp89
And a final recommendation - Ng's agentic patterns series:
https://lnkd.in/gphZ6Y5s
1
1103
2024/05/19
Compte tenu des défis actuels, nous pensons que chaque professionnel, journaliste, étudiant, citoyen devrait avoir un accès simple à des données critiques sur la crise à laquelle nous sommes confrontés.
Pour répondre à ce besoin, le Shift Project a produit un outil de visualisation de données offrant un accès immédiat et gratuit à un large éventail de statistiques mondiales sur l'énergie et le climat : le Shift Data Portal.
Faciles à parcourir et à utiliser, ces données proviennent de sources fiables et peuvent être exportées pour un usage ultérieur.
Il est possible de comparer des pays ou des organisations, de modifier les types de graphiques, les sources d'énergie, les unités ou d'autres indicateurs, le tout afin de créer des graphiques personnalisés et uniques pour illustrer ou vérifier un argument.
Découvrez le Shift Data Portal : https://swll.to/qR5i5K
Un grand merci à Scalingo, qui héberge gracieusement ce portail de données !1
777
2024/05/19
Yeah it’s definitely time to leave this planet
1
668
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2024/05/06
12
quant
2024/05/06
1
quant
2024/05/06
23
quant
Hi! I wanted to share the Python client for IBKR REST and WebSocket APIs I built recently - called IBind. It exposes existing endpoints in Python, adds conid-unwrapping, question/answer handling, parallel requests and advanced WebSocket health and subscription management. I hope you guys like it 👋
Open
2024/05/06
79
algotrading
A Multi-Agent game where LLMs must trick each other as humans until one gets caught (r/MachineLearning)
Open
2024/05/05
3
datascienceproject
2024/05/05
6
algotrading
Managing risk and enhancing investment strategies using machine learning, the future of AI in banking - a survey
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2024/05/04
0
quant
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